Persistent Intelligence, Surveillance and Reconnaissance (ISR) is playing an increasingly important role in modern warfare and has spurred the development of large-format imaging sensors that will continuously image an entire city or town at a ground sample distance (GSD) as small as 10cm. Deployed on a moving aerial platform, the opportunity naturally arises to use these sensors to create 3D stereo reconstructions of the terrain for use in mission planning and situational awareness. However, achieving acceptable depth resolution and accuracy is difficult from high altitudes. This problem is made worse by rolling shutter artifacts in the images resulting from the use of low-cost CMOS sensors. To address these challenges, we propose a system for Terrain Reconstruction Using Super-resolution Techniques (TRUST). We address the resolution problem first by combining short sequences of similar images into a single high-resolution image using super-resolution techniques. These images are then used as input into a novel multi-view stereo matching algorithm that fuses together multiple depth maps for maximum accuracy. A scheduling module ensures that images are captured far enough apart to achieve the desired voxel resolution of less than half the original GSD. An image preprocessing component removes rolling shutter distortion and other artifacts.